Overview

Brought to you by YData

Dataset statistics

Number of variables12
Number of observations39098
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.6 MiB
Average record size in memory96.0 B

Variable types

Numeric8
Categorical3
Boolean1

Alerts

cb_person_cred_hist_length is highly overall correlated with person_ageHigh correlation
cb_person_default_on_file is highly overall correlated with loan_grade and 1 other fieldsHigh correlation
loan_amnt is highly overall correlated with loan_percent_incomeHigh correlation
loan_grade is highly overall correlated with cb_person_default_on_file and 1 other fieldsHigh correlation
loan_int_rate is highly overall correlated with cb_person_default_on_file and 1 other fieldsHigh correlation
loan_percent_income is highly overall correlated with loan_amntHigh correlation
person_age is highly overall correlated with cb_person_cred_hist_lengthHigh correlation
id is uniformly distributedUniform
id has unique valuesUnique
person_emp_length has 5105 (13.1%) zerosZeros

Reproduction

Analysis started2024-10-05 07:34:34.287825
Analysis finished2024-10-05 07:34:44.338338
Duration10.05 seconds
Software versionydata-profiling vv4.10.0
Download configurationconfig.json

Variables

id
Real number (ℝ)

UNIFORM  UNIQUE 

Distinct39098
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean78193.5
Minimum58645
Maximum97742
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size305.6 KiB
2024-10-05T13:04:44.450412image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum58645
5-th percentile60599.85
Q168419.25
median78193.5
Q387967.75
95-th percentile95787.15
Maximum97742
Range39097
Interquartile range (IQR)19548.5

Descriptive statistics

Standard deviation11286.765
Coefficient of variation (CV)0.14434403
Kurtosis-1.2
Mean78193.5
Median Absolute Deviation (MAD)9774.5
Skewness0
Sum3.0572095 × 109
Variance1.2739106 × 108
MonotonicityStrictly increasing
2024-10-05T13:04:44.589983image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
97742 1
 
< 0.1%
58645 1
 
< 0.1%
58646 1
 
< 0.1%
58647 1
 
< 0.1%
58648 1
 
< 0.1%
58649 1
 
< 0.1%
58650 1
 
< 0.1%
58651 1
 
< 0.1%
58652 1
 
< 0.1%
58653 1
 
< 0.1%
Other values (39088) 39088
> 99.9%
ValueCountFrequency (%)
58645 1
< 0.1%
58646 1
< 0.1%
58647 1
< 0.1%
58648 1
< 0.1%
58649 1
< 0.1%
58650 1
< 0.1%
58651 1
< 0.1%
58652 1
< 0.1%
58653 1
< 0.1%
58654 1
< 0.1%
ValueCountFrequency (%)
97742 1
< 0.1%
97741 1
< 0.1%
97740 1
< 0.1%
97739 1
< 0.1%
97738 1
< 0.1%
97737 1
< 0.1%
97736 1
< 0.1%
97735 1
< 0.1%
97734 1
< 0.1%
97733 1
< 0.1%

person_age
Real number (ℝ)

HIGH CORRELATION 

Distinct52
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.566781
Minimum20
Maximum94
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size305.6 KiB
2024-10-05T13:04:44.716512image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile22
Q123
median26
Q330
95-th percentile39
Maximum94
Range74
Interquartile range (IQR)7

Descriptive statistics

Standard deviation6.0327608
Coefficient of variation (CV)0.21884168
Kurtosis5.6744533
Mean27.566781
Median Absolute Deviation (MAD)3
Skewness1.9424885
Sum1077806
Variance36.394202
MonotonicityNot monotonic
2024-10-05T13:04:44.842612image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
23 5059
12.9%
22 4616
11.8%
24 4305
11.0%
25 3474
8.9%
27 3102
 
7.9%
26 2578
 
6.6%
28 2454
 
6.3%
29 2198
 
5.6%
30 1531
 
3.9%
31 1222
 
3.1%
Other values (42) 8559
21.9%
ValueCountFrequency (%)
20 2
 
< 0.1%
21 1180
 
3.0%
22 4616
11.8%
23 5059
12.9%
24 4305
11.0%
25 3474
8.9%
26 2578
6.6%
27 3102
7.9%
28 2454
6.3%
29 2198
5.6%
ValueCountFrequency (%)
94 1
 
< 0.1%
84 1
 
< 0.1%
70 11
< 0.1%
69 5
< 0.1%
68 1
 
< 0.1%
66 8
< 0.1%
65 7
< 0.1%
64 4
 
< 0.1%
63 1
 
< 0.1%
62 6
< 0.1%

person_income
Real number (ℝ)

Distinct2196
Distinct (%)5.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean64060.461
Minimum4000
Maximum1900000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size305.6 KiB
2024-10-05T13:04:44.968499image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum4000
5-th percentile28800
Q142000
median58000
Q375885
95-th percentile120000
Maximum1900000
Range1896000
Interquartile range (IQR)33885

Descriptive statistics

Standard deviation37955.829
Coefficient of variation (CV)0.59250009
Kurtosis337.29215
Mean64060.461
Median Absolute Deviation (MAD)17000
Skewness10.44648
Sum2.5046359 × 109
Variance1.4406449 × 109
MonotonicityNot monotonic
2024-10-05T13:04:45.109727image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
60000 2871
 
7.3%
50000 1974
 
5.0%
30000 1414
 
3.6%
40000 1347
 
3.4%
70000 1175
 
3.0%
75000 1098
 
2.8%
45000 1075
 
2.7%
80000 1053
 
2.7%
65000 973
 
2.5%
90000 913
 
2.3%
Other values (2186) 25205
64.5%
ValueCountFrequency (%)
4000 1
 
< 0.1%
4200 1
 
< 0.1%
4800 2
 
< 0.1%
5700 1
 
< 0.1%
9600 4
 
< 0.1%
10000 2
 
< 0.1%
10008 1
 
< 0.1%
10200 1
 
< 0.1%
10800 4
 
< 0.1%
12000 15
< 0.1%
ValueCountFrequency (%)
1900000 1
 
< 0.1%
1782000 1
 
< 0.1%
1200000 2
< 0.1%
900000 1
 
< 0.1%
852240 1
 
< 0.1%
780000 3
< 0.1%
762000 2
< 0.1%
741600 2
< 0.1%
720000 1
 
< 0.1%
667680 1
 
< 0.1%
Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size305.6 KiB
RENT
20280 
MORTGAGE
16683 
OWN
2056 
OTHER
 
79

Length

Max length8
Median length4
Mean length5.6562228
Min length3

Characters and Unicode

Total characters221147
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRENT
2nd rowMORTGAGE
3rd rowRENT
4th rowRENT
5th rowMORTGAGE

Common Values

ValueCountFrequency (%)
RENT 20280
51.9%
MORTGAGE 16683
42.7%
OWN 2056
 
5.3%
OTHER 79
 
0.2%

Length

2024-10-05T13:04:45.236210image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-05T13:04:45.335948image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
rent 20280
51.9%
mortgage 16683
42.7%
own 2056
 
5.3%
other 79
 
0.2%

Most occurring characters

ValueCountFrequency (%)
R 37042
16.7%
E 37042
16.7%
T 37042
16.7%
G 33366
15.1%
N 22336
10.1%
O 18818
8.5%
M 16683
7.5%
A 16683
7.5%
W 2056
 
0.9%
H 79
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 221147
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
R 37042
16.7%
E 37042
16.7%
T 37042
16.7%
G 33366
15.1%
N 22336
10.1%
O 18818
8.5%
M 16683
7.5%
A 16683
7.5%
W 2056
 
0.9%
H 79
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 221147
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
R 37042
16.7%
E 37042
16.7%
T 37042
16.7%
G 33366
15.1%
N 22336
10.1%
O 18818
8.5%
M 16683
7.5%
A 16683
7.5%
W 2056
 
0.9%
H 79
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 221147
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
R 37042
16.7%
E 37042
16.7%
T 37042
16.7%
G 33366
15.1%
N 22336
10.1%
O 18818
8.5%
M 16683
7.5%
A 16683
7.5%
W 2056
 
0.9%
H 79
 
< 0.1%

person_emp_length
Real number (ℝ)

ZEROS 

Distinct31
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.6870684
Minimum0
Maximum42
Zeros5105
Zeros (%)13.1%
Negative0
Negative (%)0.0%
Memory size305.6 KiB
2024-10-05T13:04:45.457356image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median4
Q37
95-th percentile12
Maximum42
Range42
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.868395
Coefficient of variation (CV)0.8253336
Kurtosis2.0765186
Mean4.6870684
Median Absolute Deviation (MAD)2
Skewness1.1638143
Sum183255
Variance14.96448
MonotonicityNot monotonic
2024-10-05T13:04:45.566677image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
0 5105
13.1%
2 4817
12.3%
3 4393
11.2%
5 3844
9.8%
4 3548
9.1%
6 3423
8.8%
1 3413
8.7%
7 2714
6.9%
8 2034
 
5.2%
9 1656
 
4.2%
Other values (21) 4151
10.6%
ValueCountFrequency (%)
0 5105
13.1%
1 3413
8.7%
2 4817
12.3%
3 4393
11.2%
4 3548
9.1%
5 3844
9.8%
6 3423
8.8%
7 2714
6.9%
8 2034
 
5.2%
9 1656
 
4.2%
ValueCountFrequency (%)
42 1
 
< 0.1%
31 6
 
< 0.1%
29 1
 
< 0.1%
28 3
 
< 0.1%
26 4
 
< 0.1%
25 3
 
< 0.1%
24 8
 
< 0.1%
23 13
 
< 0.1%
22 18
 
< 0.1%
21 45
0.1%

loan_intent
Categorical

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size305.6 KiB
EDUCATION
8076 
MEDICAL
7447 
PERSONAL
6815 
VENTURE
6632 
DEBTCONSOLIDATION
5915 

Length

Max length17
Median length15
Mean length9.9623254
Min length7

Characters and Unicode

Total characters389507
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowHOMEIMPROVEMENT
2nd rowPERSONAL
3rd rowVENTURE
4th rowDEBTCONSOLIDATION
5th rowHOMEIMPROVEMENT

Common Values

ValueCountFrequency (%)
EDUCATION 8076
20.7%
MEDICAL 7447
19.0%
PERSONAL 6815
17.4%
VENTURE 6632
17.0%
DEBTCONSOLIDATION 5915
15.1%
HOMEIMPROVEMENT 4213
10.8%

Length

2024-10-05T13:04:45.676708image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-05T13:04:45.787234image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
education 8076
20.7%
medical 7447
19.0%
personal 6815
17.4%
venture 6632
17.0%
debtconsolidation 5915
15.1%
homeimprovement 4213
10.8%

Most occurring characters

ValueCountFrequency (%)
E 54156
13.9%
O 41062
10.5%
N 37566
9.6%
I 31566
8.1%
T 30751
 
7.9%
A 28253
 
7.3%
D 27353
 
7.0%
C 21438
 
5.5%
L 20177
 
5.2%
M 20086
 
5.2%
Other values (7) 77099
19.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 389507
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 54156
13.9%
O 41062
10.5%
N 37566
9.6%
I 31566
8.1%
T 30751
 
7.9%
A 28253
 
7.3%
D 27353
 
7.0%
C 21438
 
5.5%
L 20177
 
5.2%
M 20086
 
5.2%
Other values (7) 77099
19.8%

Most occurring scripts

ValueCountFrequency (%)
Latin 389507
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 54156
13.9%
O 41062
10.5%
N 37566
9.6%
I 31566
8.1%
T 30751
 
7.9%
A 28253
 
7.3%
D 27353
 
7.0%
C 21438
 
5.5%
L 20177
 
5.2%
M 20086
 
5.2%
Other values (7) 77099
19.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 389507
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E 54156
13.9%
O 41062
10.5%
N 37566
9.6%
I 31566
8.1%
T 30751
 
7.9%
A 28253
 
7.3%
D 27353
 
7.0%
C 21438
 
5.5%
L 20177
 
5.2%
M 20086
 
5.2%
Other values (7) 77099
19.8%

loan_grade
Categorical

HIGH CORRELATION 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size305.6 KiB
A
14005 
B
13604 
C
7460 
D
3269 
E
 
637
Other values (2)
 
123

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters39098
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowF
2nd rowC
3rd rowE
4th rowA
5th rowD

Common Values

ValueCountFrequency (%)
A 14005
35.8%
B 13604
34.8%
C 7460
19.1%
D 3269
 
8.4%
E 637
 
1.6%
F 105
 
0.3%
G 18
 
< 0.1%

Length

2024-10-05T13:04:45.912458image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-05T13:04:46.007210image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
a 14005
35.8%
b 13604
34.8%
c 7460
19.1%
d 3269
 
8.4%
e 637
 
1.6%
f 105
 
0.3%
g 18
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
A 14005
35.8%
B 13604
34.8%
C 7460
19.1%
D 3269
 
8.4%
E 637
 
1.6%
F 105
 
0.3%
G 18
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 39098
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 14005
35.8%
B 13604
34.8%
C 7460
19.1%
D 3269
 
8.4%
E 637
 
1.6%
F 105
 
0.3%
G 18
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 39098
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 14005
35.8%
B 13604
34.8%
C 7460
19.1%
D 3269
 
8.4%
E 637
 
1.6%
F 105
 
0.3%
G 18
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 39098
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 14005
35.8%
B 13604
34.8%
C 7460
19.1%
D 3269
 
8.4%
E 637
 
1.6%
F 105
 
0.3%
G 18
 
< 0.1%

loan_amnt
Real number (ℝ)

HIGH CORRELATION 

Distinct482
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9251.4662
Minimum700
Maximum35000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size305.6 KiB
2024-10-05T13:04:46.133149image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum700
5-th percentile2500
Q15000
median8000
Q312000
95-th percentile20000
Maximum35000
Range34300
Interquartile range (IQR)7000

Descriptive statistics

Standard deviation5576.2547
Coefficient of variation (CV)0.6027428
Kurtosis1.7847058
Mean9251.4662
Median Absolute Deviation (MAD)3000
Skewness1.204413
Sum3.6171382 × 108
Variance31094616
MonotonicityNot monotonic
2024-10-05T13:04:46.259682image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10000 4706
 
12.0%
5000 3410
 
8.7%
6000 3121
 
8.0%
12000 3026
 
7.7%
15000 2228
 
5.7%
8000 2219
 
5.7%
4000 1596
 
4.1%
3000 1514
 
3.9%
7000 1432
 
3.7%
20000 1181
 
3.0%
Other values (472) 14665
37.5%
ValueCountFrequency (%)
700 1
 
< 0.1%
900 2
 
< 0.1%
1000 243
0.6%
1050 2
 
< 0.1%
1075 1
 
< 0.1%
1200 119
0.3%
1225 1
 
< 0.1%
1250 4
 
< 0.1%
1275 1
 
< 0.1%
1300 5
 
< 0.1%
ValueCountFrequency (%)
35000 119
0.3%
34000 1
 
< 0.1%
32500 1
 
< 0.1%
32000 1
 
< 0.1%
31825 1
 
< 0.1%
30000 64
0.2%
29700 1
 
< 0.1%
28000 41
 
0.1%
27500 1
 
< 0.1%
27400 1
 
< 0.1%

loan_int_rate
Real number (ℝ)

HIGH CORRELATION 

Distinct336
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.661216
Minimum5.42
Maximum22.11
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size305.6 KiB
2024-10-05T13:04:46.401782image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum5.42
5-th percentile6.03
Q17.88
median10.75
Q312.99
95-th percentile15.65
Maximum22.11
Range16.69
Interquartile range (IQR)5.11

Descriptive statistics

Standard deviation3.0202196
Coefficient of variation (CV)0.28329035
Kurtosis-0.74360836
Mean10.661216
Median Absolute Deviation (MAD)2.74
Skewness0.18534683
Sum416832.22
Variance9.1217265
MonotonicityNot monotonic
2024-10-05T13:04:46.527143image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10.99 1412
 
3.6%
7.51 1356
 
3.5%
7.88 1162
 
3.0%
7.49 1080
 
2.8%
13.49 922
 
2.4%
7.9 864
 
2.2%
11.49 853
 
2.2%
5.42 801
 
2.0%
11.71 752
 
1.9%
6.03 721
 
1.8%
Other values (326) 29175
74.6%
ValueCountFrequency (%)
5.42 801
2.0%
5.62 1
 
< 0.1%
5.65 1
 
< 0.1%
5.79 478
1.2%
5.83 1
 
< 0.1%
5.99 364
0.9%
6 6
 
< 0.1%
6.03 721
1.8%
6.17 273
 
0.7%
6.39 55
 
0.1%
ValueCountFrequency (%)
22.11 2
< 0.1%
22.06 1
 
< 0.1%
21.74 1
 
< 0.1%
21.36 1
 
< 0.1%
21.21 2
< 0.1%
20.89 3
< 0.1%
20.86 1
 
< 0.1%
20.62 1
 
< 0.1%
20.53 1
 
< 0.1%
20.52 1
 
< 0.1%

loan_percent_income
Real number (ℝ)

HIGH CORRELATION 

Distinct63
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.15957328
Minimum0
Maximum0.73
Zeros4
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size305.6 KiB
2024-10-05T13:04:46.652544image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.04
Q10.09
median0.14
Q30.21
95-th percentile0.34
Maximum0.73
Range0.73
Interquartile range (IQR)0.12

Descriptive statistics

Standard deviation0.091633397
Coefficient of variation (CV)0.57424024
Kurtosis0.71900686
Mean0.15957328
Median Absolute Deviation (MAD)0.06
Skewness0.93909392
Sum6238.996
Variance0.0083966795
MonotonicityNot monotonic
2024-10-05T13:04:46.793578image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.13 2248
 
5.7%
0.1 2100
 
5.4%
0.08 1930
 
4.9%
0.17 1870
 
4.8%
0.07 1792
 
4.6%
0.11 1751
 
4.5%
0.09 1746
 
4.5%
0.12 1743
 
4.5%
0.14 1697
 
4.3%
0.15 1637
 
4.2%
Other values (53) 20584
52.6%
ValueCountFrequency (%)
0 4
 
< 0.1%
0.01 101
 
0.3%
0.02 268
 
0.7%
0.03 745
 
1.9%
0.04 1078
2.8%
0.05 1387
3.5%
0.06 1588
4.1%
0.07 1792
4.6%
0.08 1930
4.9%
0.09 1746
4.5%
ValueCountFrequency (%)
0.73 1
 
< 0.1%
0.63 2
 
< 0.1%
0.6 1
 
< 0.1%
0.59 1
 
< 0.1%
0.58 2
 
< 0.1%
0.57 2
 
< 0.1%
0.55 2
 
< 0.1%
0.54 1
 
< 0.1%
0.53 6
< 0.1%
0.52 3
< 0.1%

cb_person_default_on_file
Boolean

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size38.3 KiB
False
33227 
True
5871 
ValueCountFrequency (%)
False 33227
85.0%
True 5871
 
15.0%
2024-10-05T13:04:46.902954image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

cb_person_cred_hist_length
Real number (ℝ)

HIGH CORRELATION 

Distinct29
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.8307075
Minimum2
Maximum30
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size305.6 KiB
2024-10-05T13:04:46.997725image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile2
Q13
median4
Q38
95-th percentile14
Maximum30
Range28
Interquartile range (IQR)5

Descriptive statistics

Standard deviation4.0721567
Coefficient of variation (CV)0.69839839
Kurtosis3.6768174
Mean5.8307075
Median Absolute Deviation (MAD)2
Skewness1.65413
Sum227969
Variance16.58246
MonotonicityNot monotonic
2024-10-05T13:04:47.123991image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
2 7154
18.3%
3 7058
18.1%
4 7020
18.0%
8 2368
 
6.1%
6 2294
 
5.9%
9 2286
 
5.8%
5 2265
 
5.8%
7 2235
 
5.7%
10 2210
 
5.7%
12 608
 
1.6%
Other values (19) 3600
9.2%
ValueCountFrequency (%)
2 7154
18.3%
3 7058
18.1%
4 7020
18.0%
5 2265
 
5.8%
6 2294
 
5.9%
7 2235
 
5.7%
8 2368
 
6.1%
9 2286
 
5.8%
10 2210
 
5.7%
11 577
 
1.5%
ValueCountFrequency (%)
30 24
0.1%
29 12
 
< 0.1%
28 44
0.1%
27 34
0.1%
26 9
 
< 0.1%
25 23
0.1%
24 38
0.1%
23 24
0.1%
22 29
0.1%
21 20
0.1%

Interactions

2024-10-05T13:04:42.901618image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T13:04:35.756597image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T13:04:37.077787image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T13:04:38.165223image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T13:04:39.200404image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T13:04:40.122256image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T13:04:41.031542image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T13:04:41.958761image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T13:04:43.013644image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T13:04:35.905890image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T13:04:37.228665image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T13:04:38.299019image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T13:04:39.314191image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T13:04:40.230019image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T13:04:41.148495image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T13:04:42.065263image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T13:04:43.133435image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T13:04:36.096829image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T13:04:37.364866image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T13:04:38.414617image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T13:04:39.434345image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T13:04:40.347436image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T13:04:41.263974image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T13:04:42.191157image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T13:04:43.247163image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T13:04:36.279181image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T13:04:37.512118image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T13:04:38.626662image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T13:04:39.547167image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T13:04:40.466544image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T13:04:41.383095image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T13:04:42.316759image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T13:04:43.364833image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T13:04:36.397587image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T13:04:37.647132image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T13:04:38.734979image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T13:04:39.652647image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T13:04:40.585124image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T13:04:41.496334image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T13:04:42.433216image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T13:04:43.464639image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T13:04:36.547933image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T13:04:37.794800image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T13:04:38.849646image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T13:04:39.763518image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T13:04:40.687871image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T13:04:41.605425image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T13:04:42.547117image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T13:04:43.581215image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T13:04:36.682479image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T13:04:37.929385image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T13:04:38.975317image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T13:04:39.897040image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T13:04:40.798055image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T13:04:41.715524image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T13:04:42.665327image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T13:04:43.694175image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T13:04:36.904550image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T13:04:38.063544image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T13:04:39.096771image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T13:04:40.014693image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T13:04:40.917536image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T13:04:41.845740image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-05T13:04:42.782709image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2024-10-05T13:04:47.218846image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
cb_person_cred_hist_lengthcb_person_default_on_fileidloan_amntloan_gradeloan_int_rateloan_intentloan_percent_incomeperson_ageperson_emp_lengthperson_home_ownershipperson_income
cb_person_cred_hist_length1.0000.019-0.0110.0310.0180.0000.091-0.0410.8060.0370.0350.099
cb_person_default_on_file0.0191.0000.0000.0310.6510.5950.0170.0580.0160.0650.0900.010
id-0.0110.0001.000-0.0060.0090.0000.000-0.004-0.007-0.0040.000-0.003
loan_amnt0.0310.031-0.0061.0000.0750.0820.0310.7180.0510.0980.0690.365
loan_grade0.0180.6510.0090.0751.0000.6650.0230.0710.0210.0500.1220.000
loan_int_rate0.0000.5950.0000.0820.6651.0000.0220.1510.000-0.1120.130-0.094
loan_intent0.0910.0170.0000.0310.0230.0221.0000.0180.0850.0440.0920.007
loan_percent_income-0.0410.058-0.0040.7180.0710.1510.0181.000-0.050-0.0620.090-0.327
person_age0.8060.016-0.0070.0510.0210.0000.085-0.0501.0000.0630.0380.139
person_emp_length0.0370.065-0.0040.0980.050-0.1120.044-0.0620.0631.0000.1670.225
person_home_ownership0.0350.0900.0000.0690.1220.1300.0920.0900.0380.1671.0000.029
person_income0.0990.010-0.0030.3650.000-0.0940.007-0.3270.1390.2250.0291.000

Missing values

2024-10-05T13:04:43.837469image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-10-05T13:04:44.177628image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

idperson_ageperson_incomeperson_home_ownershipperson_emp_lengthloan_intentloan_gradeloan_amntloan_int_rateloan_percent_incomecb_person_default_on_filecb_person_cred_hist_length
0586452369000RENT3.0HOMEIMPROVEMENTF2500015.760.36N2
1586462696000MORTGAGE6.0PERSONALC1000012.680.10Y4
2586472630000RENT5.0VENTUREE400017.190.13Y2
3586483350000RENT4.0DEBTCONSOLIDATIONA70008.900.14N7
45864926102000MORTGAGE8.0HOMEIMPROVEMENTD1500016.320.15Y4
5586502366000RENT5.0EDUCATIOND2200014.090.33N2
6586512675000OWN10.0PERSONALB800010.620.11N4
7586522355000MORTGAGE6.0PERSONALA62506.760.12N2
8586533229124RENT0.0PERSONALC720013.110.26Y6
9586542290000RENT4.0DEBTCONSOLIDATIONC1000013.490.11Y3
idperson_ageperson_incomeperson_home_ownershipperson_emp_lengthloan_intentloan_gradeloan_amntloan_int_rateloan_percent_incomecb_person_default_on_filecb_person_cred_hist_length
39088977334072400RENT4.0PERSONALD2500014.090.35N14
39089977342442000RENT2.0EDUCATIOND1900017.270.47N4
39090977353649000RENT4.0VENTUREC1675013.980.33N15
39091977362568000MORTGAGE2.0PERSONALB280010.990.04N2
390929773728130000RENT3.0PERSONALB2000011.140.15N9
39093977382231200MORTGAGE2.0DEBTCONSOLIDATIONB300010.370.10N4
39094977392248000MORTGAGE6.0EDUCATIONA70006.030.15N3
39095977405160000MORTGAGE0.0PERSONALA150007.510.25N25
39096977412236000MORTGAGE4.0PERSONALD1400015.620.39Y4
39097977423145000RENT6.0DEBTCONSOLIDATIONB194509.910.44N9